Thanks to recent robustness studies of deep learning, we have seen attempts on giving stability certificates and/or ensuring stability at test time for fully-observed systems controlled by neural networks. Yet stability problems for neural network controlled partially observed systems remain open. Unlike fully-observed control systems where the plant states are fully revealed to the controller, most real-world control systems are only partially observed due to modeling inaccuracy, sensing limitations, and physical constraints [1]}.
Here, sensible estimates of the full system state usually depend on historical observations [2]}. Some partially observed systems are modeled using partially observed Markov decision process (POMDP) [3]} where an optimal solution is NP hard in general [4]}.
